Electrical utility service providers, or simply utilities, monitor energy usage by customers through watt-hour meters. Watt-hour meters track the amount of energy consumed, typically measured in kilowatt-hours ("kwh"), at each customer's facility. The utilities use the consumption information primarily for billing purposes.
Utilities often bill their customers based on complex rate structures wherein the different billing rates are used for different times of the day, week, or year. For example, 1 kwh of electric energy consumption at noon on a mid-summer day will likely cost the customer significantly more than 1 kwh of electric energy consumption on an autumn evening.
A primary reason for differing utility rate structures is that a utility's fixed charge costs are proportional to maximum demand. A utility must purchase and maintain enough generators to satisfy the peak demand periods, even though much of the generator capacity is unused during non-peak periods. To illustrate this concept, consider a utility with two customers. A first customer has a demand of 10,000 kw of electricity continuously for a total consumption of 240,000 kwh of electricity per day (10,000 kilowatts.times.24 hours=240,000 kilowatt-hours). A second customer requires only 12,000 kwh of electricity per day, but consumes 12,000 kw over a contiguous one hour time frame. In order to meet peak demand during the day, the utility must purchase and maintain generators that are capable of producing at least 22,000 kw of electricity: 10,000 kw for the first customer and 12,0000 kw for the second customer during peak demand. If the second customer were to divide its energy requirements over the entire day, the utility would only be required to purchase and maintain generators that produce 10,500 kw, as opposed to the 22,000 kw required under the second customer's present demand. Fixed costs on a generator, including interest on the investment, insurance on valuation, property taxes on valuation, and depreciation of equipment add up to a significant percentage of the cost of a new generator. Fixed costs on a 22,000 kw generator are typically significantly greater than fixed costs on a 10,500 kw generator. Therefore, because the second customer contributes in a much larger proportion than the first customer to the utility's fixed costs per hour of demand by the customer, the utility can justify charging customers higher rates during peak demand times.
A second reason for differing utility rate structures is to discourage energy consumption during peak demand periods. As discussed above, the utility's generators are only capable of providing a finite amount of electrical energy. Accordingly, during peak demand periods, such as on a hot day in which air conditioners consume substantial amounts of energy, the utility may be asked to deliver power in excess of its capacity, resulting in a brown-out condition or worse. The utility may avoid such a situation by increasing power capacity, purchasing power from another utility or by curtailing customer demand in peak demand situations. Increasing power capacity involves purchasing new generators, which entails a significant cost investment by the utility. Purchasing power from another utility, on the other hand, is not a reliable option because the other utilities may be contemporaneously experiencing severely high demand. By contrast controlling customer demand by varying rates involves little additional cost to the utility and is not dependent on external sources of energy. By charging higher rates during periods of peak consumption, the utility may drive down customer demand and prevent system overload.
In view of the above mentioned reasons for varying utility rate structures, it is desirable that utilities track peak demand data from its customers. Utilities may then analyze the demand data to develop a coherent billing rate structure. The advent of digital electronics in utility meters has facilitated the implementation of various features that assist in tracking such demand data.
One such feature is a Five Highest Demands (FHD) feature, which has been widely implemented. The FHD feature entails the tracing of the peak power demand times for the facility being metered. In particular, a processor of a watt-hour meter tracks the five highest demand values (FHD values), i.e., the five times that the energy consumption rate was the highest, over a limited time frame, such as a month. The information tracked by the processor is stored in a memory location within the electrical utility meter. The information stored by the FHD feature includes the amount of energy demanded during each high demand event, the date of each high demand event, and the time of day in which each high demand event occurred. The utility uses the FHD information to help identify periods of heavy load for rate structuring and resource allocation.
According to the prior art, the method of assembling the list of high demands begins by splitting a billing period into demand intervals. The demand intervals may be of any time length, but are typically between one and sixty minutes. The demand intervals may either be continuous blocks of time, or overlapping blocks of time. The meter then measures the energy demand interval. Energy demand is merely the aggregate energy consumption during the demand interval. If the meter's processor determines that the demand for any demand interval is one of the five highest of the billing period, then the FHD list is updated to include the new demand measurement (and its corresponding time period) and exclude the previous lowest demand measurement on the list.
A drawback to the above FHD method is that a single peak usage event may occur over several time intervals. In such a case, the final FHD list may contain entries from several time periods from the same peak usage event. For example, if a highest demand event on March 26 lasts for two demand intervals, then two of the five highest demand entries may identify only a single peak usage event. For example, if demand intervals are five minutes and a peak usage event occurs on March 26 from 2:00 p.m. to 2:10 p.m., then one of the entries on the FHD list the highest demand for the month of March is on March 26 at 2:00 p.m., then the second highest demand may well be on March 26 at 2:05 p.m. The resulting FHD list is of limited usefulness, because it reduces the number of discreet high usage events that are recorded.
One prior art solution to the above problem is discussed in U.S. Pat. No. 4,654,588 to Munday ("Munday"). Munday describes an FHD list wherein only one FHD may occur within any one survey period. A survey period is a window of adjacent demand intervals. For example, a survey period may be six adjacent demand intervals. Assuming that six demand intervals occur within each survey period, the system describe in Munday allows only one FHD value to be recorded from the six demand intervals found within the single survey period.
The Munday method, while reducing the likelihood that a single peak usage event will be over-represented on the the FHD list, still suffers the drawback that such an event could possibly be counted twice on the FHD list. Specifically, a single high consumption event may span from the end of one survey period to the beginning of a next survey period. As a result two entries on the FHD list may be from the single high consumption event. For example, if a customer encounters ten minute period of high demand, it is possible that the first five minutes of that high demand period could occur during the final demand interval of a first survey period, and a second five minutes of the high demand period could occur during the first demand interval of a second survey period. In this case, the single high consumption event would be split between two survey periods, and the FHD list would include two listings for the same high consumption event.
Accordingly, a need exists in the industry for an apparatus that records the five highest demands of a billing period wherein the recorded demands result from distinct high consumption events.